ModelRefs / Safety Training — AI Glossary
Safety Training — AI Glossary
Fine-tuning and RLHF processes designed to make models refuse harmful requests, avoid dangerous capabilities, and behave honestly.
Overview
Safety training encompasses: supervised fine-tuning on safe demonstrations, RLHF with human feedback penalizing harmful outputs, Constitutional AI (Anthropic's self-critique method), and red-teaming-informed data collection. Safety-capability trade-offs are actively researched; overly aggressive safety training causes refusal of benign requests ('over-refusal').
Reference details
| Topic | safety |
|---|---|
| Last reviewed | 2026-06-24 |
Related terms
Continue your research
Use these connected ModelRefs sections to compare alternatives, inspect implementation paths, and review the evidence and governance boundaries relevant to Safety Training — AI Glossary.
Frequently asked questions
What is Safety Training?
Fine-tuning and RLHF processes designed to make models refuse harmful requests, avoid dangerous capabilities, and behave honestly.
What concepts are related to Safety Training?
Closely related concepts include alignment, guardrails, refusal.